
Local precision: The system describes precisely the model in the close vicinity of the explained instance.

"Missingness": If an attribute is missing for the prediction, the method does not give it a weight, or gives it a weight of zero.

Consistency: If the explained model changes in a way that makes an attribute more important, or does not change its importance, its attributed weight is not diminished. This property is important, as some of the early prediction explanation methods could have an erratic behavior in some cases, as shown in an example of [1]}.

